194 results about "Density functional theory" patented technology

The invention provides a machine learning potential function design method for UO2 and a fission product thereof, and the method comprises the steps: S1, constructing a UO2-Xe / I unit cell with the fission product, and carrying out the optimization of the UO2-Xe / I unit cell; s2, the optimized UO2-Xe / I unit cell structure is disturbed and simulated, and a training data set is generated; s3, establishing a machine learning potential function; s4, performing molecular dynamics simulation on the machine learning potential function; s5, performing density functional theory calculation on the candidate configuration to obtain accurate structure-energy-force data, and repeating the steps S3 to S5 to perform iterative calculation; s6, verifying the trained machine learning potential function to obtain an accurate machine learning potential function; and S7, performing molecular dynamics simulation by using an accurate machine learning potential function. The precise DP model is obtained through machine learning, and a theoretical basis is provided for research of a diffusion mechanism of fission products in UO2 and molecular dynamics simulation of other mechanical properties.

A method of performing Kohn-Sham Density Functional Theory, DFT, calculations, for simulation of a material, chemical, or biological system includes: (i) constructing a model Hamiltonian of the system; (ii) inputting an electron density; (iii) constructing a Kohn-Sham potential and (iv) executing a quantum Kohn-Sham DFT algorithm iteration using the electron density and the Kohn-Sham potential to compute an updated electron density and an updated total energy. The Kohn-Sham potential is constructed by: (1) providing a second Hamiltonian based on the model Hamiltonian; (2) calculating a ground state energy of the second Hamiltonian, as a function of electron density; (3) computing a quantum-obtained exchange correlation energy functional, as a function of electron density; (4) obtaining a quantum-obtained exchange correlation potential functional by differentiation of the quantum-obtained exchange correlation energy functional; and (5) using the potential functional from (4) to construct the Kohn-Sham potential.

The invention discloses an amorphous forming ability prediction method based on Transform and table data conversion. The amorphous forming ability prediction method comprises the following steps: acquiring basic attributes and amorphous forming ability attributes of amorphous alloy composition elements in a metal alloy sample; the basic attributes refer to element properties simulated based on an experiment or a density functional theory and comprise atomic basic properties, chemical properties and physical properties, and the amorphous forming ability attributes comprise a critical casting diameter Dmax; generating an amorphous alloy data set; wherein the data set comprises an independent variable and a response variable, the independent variable comprises element composition and mixing enthalpy Hmix, and the response variable is a critical casting diameter Dmax; defining a relation model, wherein the relation model is an integrated model formed by three types of models; and training the integrated model by using an amorphous alloy data set, and constructing an amorphous forming ability evaluation model. According to the technical scheme, various relation models for predicting the critical casting diameter can be trained, and the various relation models are combined to construct the optimal amorphous forming ability evaluation model.

The invention belongs to the technical field of computational chemistry, and particularly discloses a mechanism research and analysis method for catalyzing oxidation of 2, 4-tert-butylphenol by using a double-copper biomimetic oxygen copper intermediate [Cu (II) 2O2TMP32] < 2 + >, which is based on a density functional theory (DFT), adopts a gas phase model to calculate a QM reaction mechanism, and performs structure optimization on the intermediate and a transition state; a PCM continuous medium solvation model is adopted, single-point energy correction is carried out, heat and enthalpy correction is obtained by calculating frequency analysis, the whole catalytic reaction potential energy surface is calculated by considering the Van der Waals effect, so that a possible reaction channel is obtained, and on the basis of QM calculation, possible intermediates and transition states are searched again under a QM / MM solvent model by adopting a QM / MM method, so that the calculation efficiency is improved. And determining the reaction speed step and the reaction mechanism. A complete theoretical method for simulating the system is established, a basis is provided for treating the system to develop a new solvent model, and meanwhile, an important theoretical basis and a technical reference are provided for design and development of efficient novel copper biomimetic catalytic phenol.

The invention belongs to the field of computational chemistry, and particularly relates to a multi-parameter collaborative dynamic screening method for adsorbed species on the surface of a catalyst. Comprising the following steps that an initial crystal structure is constructed and optimized through VESTA software, and a stable crystal structure model is obtained through density functional theory optimization; integrating working condition parameters such as a solvent, pH and potential, and drawing a Bayer diagram to label adsorption species and a mapping relation; and calculating a zero charge point and capacitance to obtain electrochemical parameters, and determining stable adsorption species under actual reaction through constant potential molecular dynamics simulation. According to the scheme, a deep structure-activity relationship between an active site structure and catalytic activity under a working condition is established through integration of all working condition parameters, labeling of adsorption species by a Boubye diagram, constant potential molecular dynamics simulation and the like; the problems that a systematic method combined with actual working condition parameters is lacked in prediction of the adsorbed substances on the surface of the catalyst, and real adsorbed species are difficult to accurately screen are solved, and accurate capture and efficient screening are achieved.

The invention discloses a sodium ion battery positive electrode material preparation optimization method, which comprises: calculating an electronic structure of a positive electrode material by using a density functional theory, and analyzing a reaction mechanism, conductivity and electrochemical reaction kinetics of the positive electrode material; simulating the stability of the positive electrode material in a preset environment by using molecular dynamics, predicting the phase change process of the positive electrode material under different process conditions, and optimizing preset parameters of the positive electrode material; performing preset factor evaluation on the electrochemical performance of the positive electrode material through the macroscopic simulation model; micro data of a preset method and molecular dynamics simulation are used as input and transmitted to battery performance simulation of the macroscopic simulation model, preset parameters of the positive electrode material are optimized through a multi-scale optimization algorithm, and the optimal material formula and process are output. According to the preparation optimization method of the sodium-ion battery positive electrode material, simulation results of different scales can be mutually coupled, and accurate preparation optimization of the sodium-ion battery positive electrode material is provided.

The invention discloses a method for exploring a heterogeneous catalytic reaction network based on a deep potential energy surface model, and the method comprises the steps: carrying out the accurate prediction of the free energy change of all possible element reactions on a catalyst through the combination of transfer learning and a density functional theory, and constructing a reaction network diagram based on a graph theory method; an improved Dijkstra method is used for searching a reaction network diagram to determine a reaction mechanism, and the selectivity of the catalyst is explored based on a Markov chain method. According to the technical scheme provided by the invention, when the CO2 reduction reaction mechanism on the monatomic alloy is explored, the prediction error MAE of the electron energy of each intermediate is only 0.16 eV, the calculated amount is reduced by 90%, the dominant path of the CO2 reduction reaction is visually displayed, and a new normal form is provided for complex mechanism exploration and catalyst screening.

The invention discloses a design method of GaAs3 and InAs3 thin film materials with high thermoelectric performance, and relates to the technical field of analog computation of new energy materials. The method comprises the following steps: constructing two novel two-dimensional material GaAs3 and InAs3 single-layer film structures; first principle calculation software VASP based on the density functional theory is adopted to carry out structure optimization on the two-dimensional material GaAs3 single-layer film and the InAs3 single-layer film, and structure models of the two-dimensional material GaAs3 single-layer film and the InAs3 single-layer film are obtained; the phonon spectrums are respectively calculated to ensure that the structure can exist stably; a non-equilibrium Green function method and a Landaauer-Buttiker theory are combined to systematically research and analyze the stability, the electron and heat transport characteristics and the thermoelectric figure of merit. The result shows that both the GaAs3 single-layer film and the InAs3 single-layer film have high thermoelectric conversion efficiency, and the thermoelectric figure of merit of p-type doping in the zigzag direction under 300K reaches 3.75 and 3.78 respectively. Compared with an experimental method, the method has the advantage of quickly obtaining a high-performance thermoelectric structure, and the cost is low.

The invention provides a method for evaluating insulation characteristics of C4F7N gas in a high-voltage insulation device. Comprising the following steps: importing initial parameters of a SnSe material; carrying out geometric optimization on the SnSe layer; establishing a molecular model of a C4F7N gas decomposition product; carrying out geometric optimization and energy calculation on a C4F7N gas decomposition product; constructing a calculation function of the SnSe layer; constructing a density functional theory calculation model of SnSe; establishing an adsorption model of the C4F7N gas decomposition product; the state density of the SnSe and C4F7N gas decomposition products is exported; calculating the work function change of the SnSe adsorption system in the C4F7N gas decomposition product; and evaluating the gas desorption capability of the SnSe material. According to the method provided by the invention, the C4F7N decomposition product and the influence of the C4F7N decomposition product on the insulation performance can be accurately evaluated, so that the health state of the high-voltage insulation device is effectively diagnosed, and early warning of insulation faults is realized.

The invention relates to the technical field of material science, and discloses a method for constructing a density functional tight constraint-neural network hybrid potential function, and the method comprises the steps: obtaining structure parameters of a target material sample in a data set through a constructed density functional theory data set; calculating energy, atomization energy and stress data of an electronic structure corresponding to the sample configuration based on a density functional theory; calculating band structure energy and stress of a target material sample in the density functional theory data set based on a density functional tight constraint theory, and obtaining corresponding density functional tight constraint atomization energy; calculating repulsion energy and repulsion stress of each sample configuration according to each atomization energy and the corresponding stress data; and constructing an atomic neural network potential function, and combining the atomic neural network potential function with a density functional tight constraint theory to generate a hybrid potential function. According to the method, the DFTB method and the machine learning technology are combined, the precision and the mobility of the potential function are greatly improved, and meanwhile, the calculation efficiency is remarkably improved.

The invention discloses a hydroxyl polycyclic aromatic hydrocarbon Raman spectrum detection method based on a dynamic weighted voting fusion network, relates to the technical field of hydroxyl polycyclic aromatic hydrocarbon detection, and provides an effective method which is rapid, accurate and free of chromatographic separation. Spectral information of OH-PAHs is obtained by using surface enhanced Raman spectroscopy SERS, and a Raman spectrum is obtained through density functional theory DFT calculation. A dynamic weighted voting fusion network DWVF-Net model is constructed, wherein the model comprises two core parts of cross-modal attention fusion (CMAF) and dynamic weighted voting (DWV). The CMAF dynamically weights key areas of SERS and DFT Raman spectrums through cosine similarity to realize feature fusion; and the DWV adaptively adjusts the weight of the model according to the real-time performance of the model, and optimizes an integration decision. Through the synergistic effect of CMAF and DWV, complementary information of SERS and DFT Raman spectrums is effectively fused, and the adaptability advantage of a dynamic weighting mechanism in the aspect of processing a complex data environment is verified.

A method of performing improved Kohn-Sham Density Functional Theory, DFT, calculations, for simulation of a material, chemical, or biological system includes: (i) constructing a model Hamiltonian of the system to be simulated; (ii) inputting an electron density of the system to be simulated; (iii) constructing a Kohn-Sham potential and (iv) executing a quantum Kohn-Sham DFT algorithm iteration using the electron density from (ii) and the Kohn-Sham potential from (iii) to compute an updated electron density and an updated total energy of the system to be simulated. The Kohn-Sham potential is constructed by: (1) providing a second Hamiltonian based on the model Hamiltonian; (2) calculating a ground state energy of the second Hamiltonian, as a function of electron density, using a quantum algorithm executed by a noisy intermediate-scale quantum, NISQ, processor; (3) computing a quantum-obtained exchange correlation energy functional, as a function of electron density, from the NISQ processor-obtained ground state energy of step (2); (4) obtaining a quantum-obtained exchange correlation potential functional by differentiation of the quantum-obtained exchange correlation energy functional from step (3); and (5) using the quantum-obtained exchange correlation potential functional from (4) to construct the Kohn-Sham potential.

The present invention provides an electrode with particularly excellent atmospheric stability and electron injectability, and an organic element using such an electrode. The present invention is a thin film-modified electrode including, on the electrode and adjacent: a layer containing a metal and an organic compound capable of coordinating to the metal; and a layer having a structure in which a layer containing a metal and a layer containing an organic compound capable of coordinating to the metal are adjacent to each other in that order. The thin film-modified electrode is characterized in that the organic compound is a compound in which, when the structure is optimized using the B3LYP functional / 6-31G** basis function in a calculation based on the density functional theory with aluminum atoms as the metal, the sum of the distances between metal-coordinating atoms in the organic compound and the aluminum atoms is 4.06 Å or less.

The invention provides a performance prediction method for a hafnium oxide semiconductor material structure, and relates to the technical field of semiconductor material research and development. The method comprises the following steps: performing global search in an Hf-O binary system by using crystal structure prediction software, determining the most stable two-dimensional Hf2O4 crystal structure, and further confirming the crystal configuration through structure optimization; calculating an Hf2O4 crystal structure by using density functional theory calculation software, and evaluating the dynamic, thermodynamic and mechanical stability of Hf2O4 by using a calculation result system; the electronic property of the Hf2O4 is obtained through further deep calculation, and the structural stability and semiconductor characteristics of the Hf2O4 are verified. According to the invention, through the first principle and the evolutionary algorithm calculation, the simulation calculation is used to replace the traditional trial-and-error experiment, and the novel semiconductor material with high reliability can be efficiently explored; a theoretical basis is provided for the design of a novel high-performance semiconductor material in advanced chip manufacturing, the research and development period can be expected to be remarkably shortened, and the cost can be reduced.

The invention discloses a perovskite oxide OER catalyst screening method combining deep learning and multi-task optimization, and the method comprises the steps: 1, screening out the density functional theory DFT data of perovskite oxide for OER research from a literature database, cleaning, and constructing a data set; 2, extracting features by using the central environment feature model to form a comprehensive descriptor data set; 3, training a deep neural network multi-task learning regression model based on the extracted feature data set, and optimizing the generalization ability and prediction precision of the model by combining a leave-one-out method cross validation with a loss function; 4, applying the trained model to predict and analyze a large-scale perovskite oxide structure, and implementing a multi-layer screening process, including an electric neutrality principle, a tolerance factor, an octahedral factor, overpotential evaluation and safety and economical efficiency consideration; and 5, experimental verification. The method provided by the invention has the advantages of high efficiency, accuracy, systematicness and the like, and opens up a new way for discovery and application of electro-catalytic materials.

The invention discloses a method and system for predicting stainless steel corrosion in different soil environments and a medium, and belongs to the technical field of metal corrosion prediction.The method comprises the following steps that multivariate environment data are collected; obtaining macroscopic corrosion characteristic parameters of the stainless steel in the soil simulation solution through electrochemical testing; acquiring microscopic corrosion characteristics of the stainless steel; calculating and obtaining stainless steel microscopic corrosion theoretical parameters based on a density functional theory; carrying out normalization and feature extraction on the feature parameters, and constructing a multi-dimensional feature vector; constructing a coupling prediction model of the multi-layer perceptron and support vector regression; and performing error reverse correction on the model based on actually measured data, and outputting a stainless steel corrosion rate predicted value. According to the method, a corrosion mechanism is disclosed from an atomic scale through density functional theory calculation, and a coupling prediction model of a multi-layer perceptron and support vector regression is constructed through multi-source data, so that the prediction precision is improved.

The invention relates to the technical field of pharmaceutical analysis, and discloses a method for monitoring a baicalein-betaine eutectic process based on terahertz spectroscopy, and the method comprises the following steps: mixing and ball-milling baicalein and betaine to obtain powder samples, collecting the powder samples with different ball-milling times, drying, mixing with a dispersing agent, tabletting, and drying to obtain the baicalein-betaine eutectic crystal. The method comprises the following steps: measuring spectral data by using terahertz time-domain spectroscopy equipment, selecting a characteristic peak and tracking intensity change to obtain a curve, carrying out nonlinear fitting on the curve, obtaining a molecular vibration mode through a density functional theory, and analyzing the eutectic process by combining the two modes. The terahertz spectrum technology is combined with theoretical calculation, complete analysis of the baicalein-betaine eutectic process from macrodynamics monitoring to microcosmic molecular action mechanism analysis is achieved, and an effective means is provided for eutectic formation research and process optimization.

The invention discloses a method and system for simulating the operating environment of a hydrogen energy reaction kettle, and the method comprises the steps: building a multi-scale simulation frame based on the hydrogen energy reaction kettle, and carrying out the modeling of a micro scale, a mesoscale and a macro scale of the hydrogen energy reaction kettle; performing multi-physical field coupling simulation on the hydrogen energy reaction kettle, including coupling calculation of a flow field, a temperature field, a concentration field and a stress field in an operation environment of the hydrogen energy reaction kettle; performing intelligent optimization simulation based on a multi-scale simulation framework and multi-physics field coupling simulation, wherein the intelligent optimization simulation comprises adaptive grid optimization, non-uniform inlet simulation and two-phase flow simulation; and constructing a digital twin platform, and carrying out system-level dynamic simulation and intelligent control on the hydrogen energy reaction kettle based on the digital twin platform. According to the method, all-around modeling and simulation from a micro scale to a macro scale are realized through a plurality of methods such as a density functional theory, molecular dynamics, a lattice Boltzmann method and computational fluid dynamics.

The invention discloses a prediction method for evaluating the surface silanization reaction efficiency of white carbon black, and the method comprises the steps: collecting the structural information of ionic liquid in an object to be evaluated, drawing the structural formula of the corresponding ionic liquid by using chemdraw software, then importing chem3D software, storing the structural formula as a gjf file, loading Gaussian 09 software, optimizing the minimum point based on the density functional theory, and calculating the surface silanization reaction efficiency of the object to be evaluated. A chk file containing minimum point wave function information is obtained, and a Gaussian 09 built-in formchk tool is used for converting the chk file into an fchk file; and loading the fchk file into a Multiwfn program, and carrying out wave function analysis on the obtained minimum point to obtain the molecular surface quantitative descriptor for evaluating the catalytic activity of the ionic liquid. The method is a method based on the density functional theory and molecular surface quantitative analysis, the obtained molecular surface quantitative descriptor has good correlation with the ILs catalyzed white carbon black and silane coupling agent reaction efficiency data set obtained by a model compound experiment, and the method has reliability and practicability.

An information processing device according to an embodiment is provided with at least one memory and at least one processor. The at least one processor: calculates, at each of a plurality of positions in a system to be calculated, a descriptor based on values obtained by convolving results obtained by performing a linear operation on respective eigenfunctions of a plurality of electrons in an equation used in a density functional theory; inputs at least the descriptor to a trained model at each of the plurality of positions and outputs the exchange correlation energy density of each of the plurality of electrons; applies the exchange correlation energy density to the equation at each of the plurality of positions and calculates the eigenfunction of each of the plurality of electrons and the eigenenergy of each of the plurality of electrons; and calculates the energy of the system by using the eigenfunctions and the eigenenergies.

The invention provides an evaluation method and evaluation equipment for the free adsorption proportion of deep coal rock gas, and relates to the technical field of coal rock gas development. The method comprises the following steps: constructing micro-pore composite models with different rock pore diameters based on a to-be-evaluated coal rock sample; wherein the micro-pore composite model is constructed based on the material composition of the to-be-evaluated coal rock sample and the full-scale pore characteristics of the coal rock sample; setting simulation environment parameters of the micro-pore composite model, adsorbing preset gas into the micro-pore composite model, enabling the micro-pore composite model to reach balance based on a density functional theory, and establishing a dynamic evolution model by changing the pore diameter of the micro-pore composite model to obtain simulation data under different simulation environment parameters; and based on the simulation data under different simulation environment parameters and a preset free adsorption gas proportion model, determining the deep coal rock gas free adsorption proportion. According to the method, the accuracy of the free adsorption proportion of the deep coal rock gas can be improved.

The invention discloses a simulation modeling method and device for a nanoscale device, relates to the technical field of semiconductor modeling and simulation, and aims to solve the problem that efficiency and precision cannot be considered in nanoscale device modeling and simulation in the prior art. The method comprises the steps that key material parameters are extracted from electronic structure basic data of a target nanoscale device, a default material parameter library in TCAD software is corrected based on the key material parameters, and a corrected material parameter library is obtained; electronic structure basic data is obtained based on a density functional theory; based on the corrected material parameter library, optimizing adjustable parameters of a ballistic transport model preset in TCAD software in combination with a transport IV curve to obtain a ballistic transport model after parameter optimization; the transportation IV curve is generated by adopting a quantum transportation method based on an unbalanced Green function in combination with key material parameters; and integrating the key material parameters with the ballistic transport model after parameter optimization, and constructing a simulation parameter library of the target nanoscale device.

The invention discloses a chiral sample classification method for mid-infrared spectroscopy, which comprises the following steps: acquiring sample data by using a Fourier infrared spectroscopy system, calculating the transmittance and absorption coefficient of a sample in an infrared band, and recording the data as experimental data of a chiral isomer; constructing a sample molecular structural formula; electron density and energy of molecules are calculated, energy of different excited states and corresponding wave functions are calculated by using a time-dependent density functional theory, the energy of the excited states is converted into frequency, and the absorption peak frequency of the material is obtained and serves as theoretical data of a chiral isomer; extracting a chiral isomer frequency band based on a density functional theory; and carrying out feature classification by using a support vector machine. According to the method, characterization analysis of sample vibration spectrum information and detection results can be achieved, effective vibration frequency bands in the molecular structure are extracted based on the density functional theory, theoretical support is provided for chiral molecule identification, and the accuracy and generalization ability of classification results are further improved.

The invention discloses an analysis method of an XLPE thermo-oxidative aging structure evolution and insulating property correlation mechanism based on microscopic parameters. The analysis method comprises the following steps: step 1, constructing a periodic structure model of an oxygen-containing cross-linked XLPE and carrying out geometric optimization; step 2, acquiring XLPE structure models under different thermo-oxidative aging degrees by utilizing de novo molecular dynamics; 3, on the basis of a density functional theory (DFT), calculating microcosmic parameters, related to insulation performance, of XLPE of different structures under different electric field intensities by utilizing Gaussian software; and 4, depicting a'thermal oxidation aging degree-microscopic parameter 'radar map based on the change rule of the microscopic parameters of the XLPE under different thermal oxidation aging degrees, and constructing a structure-function relationship between the microscopic parameters and the different thermal oxidation aging degrees. According to the method, the thermo-oxidative aging process of the XLPE is simulated on the basis of AIMD, differences of microcosmic parameters of the XLPE under different aging degrees are compared, and the structure-function relationship between the thermo-oxidative aging degrees and the microcosmic parameters of the cross-linked XLPE is constructed more accurately.

The invention discloses an environment-friendly mixed insulating gas liquefaction temperature prediction method based on mean square displacement MSD and conformational analysis. The method comprises the following steps that 1, a system model of environment-friendly insulating gas and a gas mixture of the environment-friendly insulating gas is established; step 2, utilizing Materials Studio simulation software to simulate the molecular dynamics process of the environment-friendly mixed insulating gas at different temperatures based on an NPT ensemble; 3, analyzing mean square displacement MSD of the environment-friendly mixed insulating gas based on a molecular dynamics simulation result, and calculating average mean square displacement; 4, analyzing the conformational change of the environment-friendly mixed insulating gas based on a molecular dynamics simulation result; and 5, predicting the liquefaction temperature of the environment-friendly mixed insulating gas by integrating the mean square displacement and the conformation analysis result. Based on the density functional theory, the liquefaction temperature of the environment-friendly mixed insulating gas is conveniently and quickly predicted through molecular dynamics simulation.

The invention relates to a characterization method of a thermal decomposition mechanism of an azo initiator. The characterization method comprises the following steps: (1) carrying out thermal stability analysis on the azo initiator; (2) acquiring microscopic decomposition characteristic data of the azo initiator by using a thermogravimetric-Fourier transform infrared spectroscopy combined technology; (3) constructing a three-dimensional molecular structure model of the azo initiator; (4) calculating the bond dissociation energy of the key covalent bond based on the density functional theory; and (5) the thermal decomposition characteristic temperature, the heat release behavior, the decomposition product and the evolution path of the azo initiator are determined by integrating the experimental data measured by the combination technology and the bond dissociation energy obtained by theoretical calculation, and the complete thermal decomposition mechanism of the azo initiator is clarified. According to the method, the thermal decomposition rule of the azo initiator is disclosed comprehensively and accurately through a technical path of combining experimental analysis and theoretical simulation and verifying macroscopic characteristics and micromechanism, and a key theoretical basis and technical support are provided for preventing and controlling the thermal runaway risk of such substances.

The invention relates to the technical field of information communication particularly suitable for specific application fields, and discloses a machine learning-based ionic liquid screening method for catalyzing CO2 cycloaddition reaction. The method comprises the following steps: constructing an ionic liquid three-dimensional structure database; calculating and generating a three-dimensional electrostatic potential and electron density data field through a density functional theory; voxelizing the data field into a standardized multi-channel three-dimensional tensor; utilizing a three-dimensional convolutional neural network regression model to automatically learn space electron characteristics and predict catalytic activity; and performing high-throughput virtual screening on the candidate library. Through end-to-end learning of the mapping relation between the three-dimensional electronic structure and the catalytic activity of the ionic liquid, the prediction accuracy and the screening efficiency are improved.

A method comprising providing a training data set to a machine learning (ML) system, the training data set indicative of density functional theory (DFT) data for a plurality of materials, the DFT data representing atomic configurations for the plurality of materials, determining a fingerprint for the atomic configuration for each of the plurality of materials, inputting the fingerprints into a machine learning system, generating, with the machine learning system, electron charge density data for the plurality of materials, inputting the electron charge density data and the fingerprint into a machine learning system, and predicting, with the machine learning system, one or more DFT properties of the plurality of materials.

The invention discloses a method for predicting the lowest triplet excited state energy of receptor micromolecules, which comprises the following steps of: acquiring data and executing density functional theory (DFT) calculation to construct a database containing molecules and ET1 energy (namely descriptors) of construction fragments of the molecules; according to an ITIC series molecular modification strategy, statistical analysis is carried out on the structural features of all the construction units, and a redundant factor sigma exc is obtained; and predicting the T1 of the molecule through a machine learning algorithm by using the descriptor modified by the redundant factor sigmaexc. According to the method, the correction parameter of the redundant factor is provided and is applied to the rapid design and screening framework of the high-performance A-D-A OPV material of the ML algorithm. On the basis of the structure-function relationship, under modification of redundant factors, the photovoltaic characteristics of the molecules are coupled by means of constructing unit prediction molecules T1 and applying machine learning mathematics to quantify the data relationship, and the method has good interpretability. Through the trained ML model, direct, rapid and accurate ET1 prediction based on the molecular construction unit is realized.

The invention provides an iron-sulfur isotope equilibrium fractionation factor calculation method based on a density functional theory. The iron-sulfur isotope equilibrium fractionation factor calculation method comprises the following steps: firstly, constructing initial mineral models of amorphous FeS, cubic FeS, maquinol ore, hematite, hematite and pyrite; the model is optimized through a spin polarization DFT + U method, GGA-PBE functional, OTFG ultra-soft pseudo potential, 400 eV plane wave cutoff energy and 5.0 * 10 <-7 > Hartree self-consistent field convergence standards are selected as calculation parameters, and the optimal Hubbar Ueff value and magnetic sequence arrangement of each mineral are determined; and finally, calculating the simple harmonic vibration frequency of the mineral, and deducing equilibrium fractionation factors (1000ln beta value) of Fe and S isotopes at a specific temperature. According to the method, the theoretical blank of isotope fractionation of the conversion path from amorphous FeS to pyrite is filled up, the data can be used for judging the isotope balance of minerals and tracing the mineralization process, and an important theoretical basis is provided for research on sedimentary diagenesis and ancient ocean redox.